Gas differentiating sensor suite

ABSTRACT

A gas leak detection system that combines sensor units having an array of sensors that detect natural gas and the volatile organic compounds and variable atmospheric conditions that confound existing gas leak detection methods, a specially designed sensor housing that limits the variability of those atmospheric conditions, and a machine learning-enabled process that uses the wide array of sensor data to differentiate between natural gas leaks and other confounding factors. Multiple low-cost sensor units can be used to monitor gas concentrations at multiple locations across a site (e.g., a well pad or other oil or natural gas facility), enabling the gas leak detection system to model gas leak emission rates in two- or three-dimensional space to reveal the most likely origin of the gas leak.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Prov. Pat. Appl. No.63/184,669, filed May 5, 2021, U.S. Prov. Pat. Appl. No. 63/292,805,filed Dec. 22, 2021, and U.S. Prov. Pat. Appl. No. 63/292,763, filedDec. 22, 2021, which are hereby incorporated by reference.

FEDERAL FUNDING

None

BACKGROUND

Reducing air pollution and greenhouse gasses are critically importantsteps toward slowing the rate of climate change and improving overallair quality. Therefore, detecting natural gas leaks and identifying theorigin of those natural gas leaks are important tasks, particularly atwell pads and other oil or natural gas facilities.

Inexpensive metal oxide gas sensors are quite sensitive to natural gasessuch as methane and ethane, but that sensitivity is swamped by theirsensitivity to volatile organic compounds that may be present in theatmosphere, variations in the atmospheric moisture content andtemperature, and even the temperature of the sensor housing itself.Additionally, while metal oxide sensors have a relatively long life spancompared to other types of sensors like photoionization detectors (PIDs)and chemically reactive sensors, metal oxide sensors are subject todegradation and/or a change in sensor response over time. Therefore,prior art natural gas detection methods have been unable to utilizeinexpensive metal oxide gas sensors in a way that accuratelydifferentiates between sensor responses to natural gas leaks and sensorresponses to other, unrelated conditions.

Instead, existing methods include collecting air samples to be analyzedat a lab or using science-grade instruments, high-precision handheld gasmeasurement instruments, or optical gas imaging (OGI) cameras. In eachinstance, the high cost of the equipment and the need for humanoperators prevents those methods from being used to continuously monitoran array of locations across the site of a potential gas leak.Furthermore, because the number of the air sampling locations islimited, existing methods do not provide a sufficient number ofobservations to identify the origin of a gas leak or estimate the rateof a gas leak. Finally, those expensive and time intensive methods arepoorly suited for citizen-driven monitoring (e.g., in neighborhoods thatmay be near buried, leaking pipelines or active drilling sites) that canhelp detect and pinpoint locations of potential leaks before they reachdangerous, explosive levels.

Accordingly, there is a need for a system that uses inexpensive metaloxide sensors to detect gas leaks from a number of locations and modelsthe emissions and dispersion of those gas leaks to reveal the likelyorigin of those gas leaks. To do so, there is a need for a system thatdifferentiates between the response of metal oxide gas sensors tonatural gas and the response of those sensors to unrelated conditions,including other volatile organic compounds in the atmosphere, variationsin the atmospheric moisture content and temperature, and the temperatureof the sensor housing itself.

SUMMARY

Disclosed is a gas leak detection system that combines sensor unitshaving an array of sensors that detect natural gas and the volatileorganic compounds and variable atmospheric conditions that confoundexisting gas leak detection methods, a specially designed sensor housingthat limits the variability of those atmospheric conditions, and amachine learning-enabled process that uses the wide array of sensor datato differentiate between natural gas leaks and other confoundingfactors.

To differentiate between natural gas and volatile organic compounds, themachine learning-enabled process takes advantage of the varyingresponsiveness of each sensor in the array to measure the concentrationsof both natural gas and volatile organic compounds. The machinelearning-enabled process can also easily incorporate additional datafrom additional sensors (e.g., additional gas sensors, directionalmicrophones, etc.) to detect other gases, to more accurately detectnatural gas leaks, etc.

The sensor units can autonomously and continuously monitor potential gassources, even in remote locations without access to power, and selectthe best available communication network to maintain communication witha remote monitoring system. Because the sensor units are low cost,multiple sensor units can be used to monitor gas concentrations atmultiple locations across a site (e.g., a well pad or other oil ornatural gas facility), enabling the gas leak detection system to modelgas leak emission rates in two- or three-dimensional space to reveal themost likely origin of the gas leak.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of exemplary embodiments may be better understood with referenceto the accompanying drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of exemplary embodiments.

FIG. 1 is a diagram of a gas leak detection system, including sensorunits deployed at a site and a remote data analysis and reportingsystem, according to an exemplary embodiment of the present invention;

FIG. 2A is a diagram of a sensor suite of a sensor unit according to anexemplary embodiment;

FIG. 2B is a diagram of the basic measuring circuit of a metal oxidesensor;

FIG. 2C is a diagram of a sensor heater circuit for a metal oxide sensoraccording to an exemplary embodiment;

FIG. 2D is a graph depicting an active temperature modulation processaccording to an exemplary embodiment;

FIG. 2E is an image of the sensor suite of FIG. 2A according to anexemplary embodiment;

FIG. 2F is an image of the sensor suite of FIG. 2A inside a sealedsensor chamber according to an exemplary embodiment;

FIG. 3A is a block diagram of a sensor unit according to an exemplaryembodiment;

FIG. 3B is an exterior view of the sensor unit according to an exemplaryembodiment;

FIG. 3C is an interior view of the sensor unit according to an exemplaryembodiment;

FIG. 4 is a flowchart illustrating a gas leak detection processaccording to an exemplary embodiment;

FIG. 5A is an image that depicts the gas leak detection system deployedfor methane emissions testing and evaluation;

FIG. 5B is a graph of the emission rates that were measured by the gasleak detection system when gas was not released;

FIG. 5C is a graph of the emission rates that were measured by the gasleak detection system when gas was released;

FIG. 5D is a graph of the methane concentration measured when gas wasnot released;

FIG. 5E is a graph of the methane concentration measured when gas wasreleased;

FIG. 5F is a graph of the emission rate measured by a first sensor unitduring a selected time period of methane emissions testing andevaluation;

FIG. 5G is a graph of the emission rate measured by a second sensor unitduring a selected time period of methane emissions testing andevaluation;

FIG. 6 is a flowchart of a gas leak mapping process according to anexemplary embodiment;

FIG. 7A is a flowchart of an automated distance and bearingidentification process according to an exemplary embodiment;

FIG. 7B is a flowchart of a manual distance and bearing identificationprocess according to an exemplary embodiment;

FIG. 8A is a flowchart of an automated wind direction identificationprocess according to an exemplary embodiment;

FIG. 8B is a diagram of a manual wind direction identification process800 b according to an exemplary embodiment;

FIG. 9 is a flowchart illustrating a background concentrationcalculation process according to an exemplary embodiment;

FIG. 10 is a flowchart illustrating a background concentrationadjustment process according to an exemplary embodiment;

FIG. 11 is a flowchart illustrating an example solar irradiancecalculation process;

FIG. 12 is a flowchart illustrating a sky cover estimation processaccording to an exemplary embodiment;

FIG. 13 is a flowchart illustrating a surface layer air mixingconditions estimation process according to an exemplary embodiment;

FIG. 14 is a flowchart illustrating a gas transport model according toan exemplary embodiment;

FIG. 15A is a flowchart illustrating a wind-cell intersection trackingarray update process according to an exemplary embodiment;

FIG. 15B is a diagram illustrating a process for identifying the mostprobable leak location according to an exemplary embodiment;

FIG. 15C is a diagram illustrating a subsequent step in the process foridentifying the most probable leak location according to an exemplaryembodiment;

FIG. 16 is a flowchart illustrating a process for calculating the totalemissions for a user-selected time period and user-selected locationaccording to an exemplary embodiment;

FIG. 17 is a flowchart illustrating an emissions data output processaccording to an exemplary embodiment;

FIG. 18A is a view of the dashboard interface according to an exemplaryembodiment;

FIG. 18B is another view of the dashboard interface according to anexemplary embodiment;

FIG. 18C is another view of the dashboard interface according to anexemplary embodiment;

FIG. 18D is another view of the dashboard interface according to anexemplary embodiment;

FIG. 18E is another view of the dashboard interface according to anexemplary embodiment; and

FIG. 18F is another view of the dashboard interface according to anexemplary embodiment.

DETAILED DESCRIPTION

Reference to the drawings illustrating various views of exemplaryembodiments is now made. In the drawings and the description of thedrawings herein, certain terminology is used for convenience only and isnot to be taken as limiting the embodiments of the present invention.Furthermore, in the drawings and the description below, like numeralsindicate like elements throughout.

Disclosed is a gas leak detection system 100 that combines sensor unitshaving an array of sensors that detect natural gas and the volatileorganic compounds and variable atmospheric conditions that confoundexisting gas leak detection methods, a specially designed sensor housingthat limits the variability of those atmospheric conditions, and amachine learning-enabled process that uses the wide array of sensor datato differentiate between natural gas leaks and other confoundingfactors.

System Architecture

FIG. 1 is a diagram of a gas leak detection system 100 deployed at asite 10 according to an exemplary embodiment of the present invention.In the example embodiment of FIG. 1, the site 10 is an oil or naturalgas well pad (e.g., a producing facility, a pre-production orearly-production well pad, a plugged or abandoned or orphaned well pad,etc.) that includes a well head 12, a separator 14, a storage tank 16,etc., as well as a nearby neighborhood 18.

As shown in FIG. 1, the system 100 includes one or more sensor units 120at each site 10, which are in communication with a data analysis andreporting system 180 via communication networks 50. For each site 10,the system 100 includes at least one anemometer 130 (e.g., Argent DataWind Sensors) that measures the wind speed and direction at or near thesite 10.

The data analysis and reporting system 180 includes at least one server182 and non-transitory computer readable storage media 186. The server182 may be any hardware computing device having a hardware computerprocessor capable of performing the functions described below. The dataanalysis and reporting system 180 may also receive data from one or morethird party data sources 60 via the communications networks 50.

The communications networks 50 may include short-range wireless networks51 (e.g., a mesh network formed by the sensor units 120 at the site 10),cellular networks 53, WiFi networks 55, satellite communicationsnetworks 57 (e.g., the Iridium Satcom system, SpaceX Starlink, etc.),and the Internet 59.

In some embodiments, some or all of the sensor units 120 deployed at thesite 10 have a parent-child relationship, where secondary sensor units120A and 120B output sensor data to a primary sensor unit 120P (e.g.,using direct, short range, wireless communication) and the primarysensor unit 120P forwards the sensor data collected by both the primarysensor unit 120P and the secondary sensor units 120A and 120B to thedata analysis and reporting system 180 (e.g., via a cellular network 53or a WiFi network 55 and the Internet 59). In other embodiments, each ofthe one or more sensor units 120 deployed at the site 10 output sensordata to the data analysis and reporting system 180 directly (e.g., via acellular network 53, a WiFi network 55, or a satellite communicationsnetwork 57).

The sensor units 120, which are described in detail below with referenceto FIGS. 2A-3E, collect air samples at the site 10, enabling the gasleak detection system 100 to detect gas leaks at the site 10. To do so,each sensor unit 120 includes an intake port 122 (e.g., a PVC pipe)and/or intake tube 123 for collecting air samples and an exhaust port124 and/or exhaust tube 125 for exhausting those collected air samples.The ends of the intake tube 123 and the exhaust tube 125 may beprotected within the larger intake port 122, which may include screensto protect the openings of the intake tube 123 and the exhaust tube 125from dust, snow, insect nesting, etc.

Each sensor unit 120 includes a power source 128. In most embodiments,the power source 128 is a solar panel, enabling the sensor unit 120 toself-sufficiently operate in any location that receives light from thesun. In some instances, however, a sensor unit 120 may be used in alocation with a consistent power source 128 (e.g., an AC power source, avehicle or other battery, etc.).

To collect air samples above ground level, one or more of the sensorunits 120 may include a mast 126. To collect air samples at auser-selectable height, the height of the mast 126 may be adjustable. Insome embodiments, a sensor unit 120 may be mounted on the mast 126. Thesensor units 120P and 120B of FIG. 1, for example, are shown mounted onmetal masts 126, enabling sensor units 120P and 120B to collect airsamples above ground level via an intake port 122. In other embodiments,a sensor unit 120 may be located near a mast 126 and include an intaketube 123 affixed to the mast 126 to collect air samples above groundlevel. In those embodiments, the mast 126 may be a light weight,insulative material (e.g., fiberglass) on a wide base 127 to prevent themast 126 from being struck by lightning and to reduce the likelihood ofthe mast 126 being knocked over during a storm. In those embodiments,the mast 126 may also be hollow, enabling the intake tube 123 to beplaced inside the mast 126. The sensor unit 120A of FIG. 1, for example,is shown mounted on a base 127 with a telescoping fiberglass mast 126,enabling a sensor unit 120A to collect air samples via the intake tube123 extending to the top of the mast 126. In some embodiments, a sensorunit 120 may include two intake tubes 123, enabling the sensor unit 120to collect air samples from two different heights (for example, at thetop of the mast 126 and at the middle of the mast 126). In addition toincreasing the likelihood that at least one of the intake tubes 123 willintercept the emissions plume of a gas leak, collectingspaced-vertically air samples helps to define the spatial distributionof gas within the plume, which can be used within a plume model (asdescribed below with reference to FIGS. 6-17) to better define what thelikely emissions rate is at the leak source.

The particular way that the sensor units 120 are deployed will depend onthe structures on a particular site 10, how those structures arearranged on the site 10, and the typical wind conditions. Windconditions (e.g., lack of winds) are an important factor in thisconsideration, since they transport trace gases from the gas leak to thesensor unit 120. Sites 10 where the wind direction varies relativelyuniformly may allow for use of only a single sensor unit 120 in acentral location relative to the structures on the site 10. In thoseinstances, if the intake port 122 or the opening of the intake tube 123is relatively high above ground, the centrally-located sensor unit 120can rely on the different wind directions to advect gases to the sensorunit 120 from locations elsewhere on the site 10 and can detect gasesreleased during calm conditions because the plume will rise nearlyvertically and disperse laterally in a uniform way. If the winddirection at a site 10 tends to be distributed mostly only along twomain axes, which is common, then two sensor units 120 would likely beneeded, preferably arranged parallel to the prevailing wind direction.When wind directions are more variable, or when a site is particularlylarge, three or more sensor units 120 may be deployed.

As shown in FIG. 1, a sensor unit 120 may also be installed in a nearbyneighborhood 18 (e.g., downwind of a potential gas source). In eachinstance, the sensor unit 120 may communicate via a WiFi network 55 ifavailable or a cellular network 53 or satellite communications network57 if a WiFi network 55 is unavailable.

A version of the sensor unit 120 may also be mounted on a vehicle 40,such as a motor vehicle 42 or (manned or unmanned) aircraft 48, forexample if more in-depth spatial mapping of gas plume dimensions andcharacteristics is desired. That approach can be extended to combine anetwork of mobile sensor units 120 with readings available in real timeat the data analysis and reporting system 180 (e.g., to direct sensorunit 120 operators to different locations to help pinpoint leaksources).

Sensor Suite

FIG. 2A is a diagram of a sensor suite 200 of a sensor unit 120according to an exemplary embodiment.

As described above, metal oxide gas sensors are inexpensive, durable,and are sufficiently sensitive to natural gases (methane, ethane) foremissions monitoring needs. However, the sensitivity of metal oxidesensors to natural gases is swamped by their sensitivity to volatileorganic compounds that may also be present in the atmosphere. If theconcentration of total volatile organic compounds (TVOCs) in an airsample could be accurately determined, their effect on the sensor dataoutput by metal oxide gas sensors could be subtracted out. However, thepresence of methane also reduces the sensitivity of VOC detectors (e.g.,photoionization detectors). Therefore, existing methods have been unableto differentiate between changes in the sensor output of metal oxide gassensors due to a natural gas leak and changes in that sensor outputcaused by the presence of volatile organic compounds.

The gas leak detection system 100 overcomes the drawback ofcross-sensitivities to different gases by combining a machinelearning-enabled gas leak detection process 400 (described below withreference to FIG. 4) and an array of metal oxide sensors 220 that eachexhibit a different type and range of sensitivity to methane andvolatile organic compounds. In the embodiment of FIG. 2A, the array ofmetal oxide sensors 220 includes a methane-sensitive metal oxide sensor(MOS) 240, a VOC-sensitive MOS 260, and a VOC-filtered MOS 280. Themethane-sensitive MOS 240 may be any metal oxide sensor configured tooutput an indication of the concentration of methane in air samples(e.g., a Figaro TGS2600). The VOC-sensitive MOS 260 may be any metaloxide sensor configured to output an indication of the concentration ofvolatile organic compounds in air samples (e.g., a Figaro TGS2602). TheVOC-filtered MOS 280 may be any metal oxide sensor that includes avolatile organic compound filter and outputs an indication of theconcentration of methane and ethane in filtered air samples (e.g., aFigaro TGS2611). The volatile organic compound filter of theVOC-filtered MOS 280 may be any filter (e.g., a charcoal filter) thatfilters air samples and outputs filtered air samples having a lowerconcentration of volatile organic compounds than the unfiltered airsamples.

Each of the metal oxide sensors 220 exhibit a different type and rangeof sensitivity to methane and volatile organic compounds. Themethane-sensitive MOS 240 is sensitive to relatively low concentrationsof methane in an air sample, but is affected by the presence of volatileorganic compounds in the air sample. The VOC-sensitive MOS 260 issensitive to volatile organic compounds in the air sample, but isaffected by the presence of methane in the air sample. Because theVOC-filtered MOS 280 includes a volatile organic compound filter, it issignificantly less sensitive to volatile organic compounds than themethane-sensitive MOS 240. However, increasingly larger amounts of VOCfiltration also reduces methane sensitivity of the VOC-filtered MOS 280relative to the methane-sensitive MOS 240.

Because each of the metal oxide sensors 220 exhibit a different type andrange of sensitivity to methane and volatile organic compounds, the gasleak detection system 100 is able to separately determine theconcentrations of both methane and volatile organic compounds. Using themachine learning-enabled gas leak detection process 400 described below,the gas leak detection system 100 can predict the response of the arrayof metal oxide sensors 220, assuming that no methane or VOCs are presentin the air sample. The system 100 then calculates the difference betweenthe sensor data output by the array of metal oxide sensors 220 inresponse to the air sample and that predicted response and converts theresponse difference to a measured concentration of methane and othergases.

Another drawback of metal oxide sensors discussed above is they are alsosensitive to variations in the atmospheric conditions of the air sample(primarily the moisture content of the air sample and secondarily thetemperature of the air sample). To remove the temperature and humidityeffects from the sensor data output by the metal oxide sensors 220, thegas leak detection system 100 identifies the temperature and moisturecontent of the air sample, uses the machine learning-enabled gas leakdetection process 400 described below to predict the sensor response ofthe array of metal oxide sensors 220 for the given temperature andmoisture content of the air sample, calculates the difference betweenthe sensor data output by the array of metal oxide sensors 220 and thatpredicted response, and converts the response difference to a measuredconcentration of methane and other gases.

To identify the temperature and moisture content of the air sample, thesensor suite 200 also includes at least one temperature and relativehumidity sensor 210 configured to output indications of the temperatureand relative humidity of the air samples (e.g., a Renesas HS3001). Giventhe importance of removing the temperature and humidity effects,preferred embodiments of the sensor suite 200 include at least twotemperature and relative humidity sensors 210 a and 210 b, preferablywith different sensitivity and response times. (For example, the sensorsuite 200 may include both a Renesas HS3001 and a Bosch SensortecBME680, which also outputs an indication of the concentration ofvolatile organic compounds in air samples). Those differentsensitivities and response times help address the small mismatch in theresponse times of the metal oxide sensors 220 and the temperature andrelative humidity sensors 210 to changes in temperature and humidity,improving the temperature and relative humidity correction performed bythe system 100.

The sensor unit 120 also further reduces variations in the temperatureand humidity of the air sample by enclosing the sensor suite 200 in asealed sensor chamber 201 that is in flow communication with the intaketube 123 and the exhaust tube 125. The intake tube 123 includes asoftware-controlled intake pump 223 that introduces the air sample intothe sensor chamber 201. The metal oxide sensors 220 generate heat as anatural byproduct of the air sampling process and heat the air sample.Because the sensor suite 200 is enclosed in a sealed sensor chamber 201,the temperature of the air sample is largely a function of the length ofthe exposure time period specified by the system 100 and is thereforeless dependent on variations in the outside air temperature.Accordingly, the sensor unit 120 takes advantage of the heat generatedby the metal oxide sensors 220 to provide a more consistent range oftemperature and humidity conditions for the sampled air.

The length of the exposure time period is typically 10 seconds and canbe selected to optimize system performance. Regardless of the selectedlength of the exposure time period however, sampling air at a site 10over an exposure time period having a consistent length reduces thevariability caused by changes in the outside air temperature andimproves the performance of the metal oxide sensors 220.

To further reduce variations in the moisture content of the air samples,the intake tube 123 may have a moisture-blocking design and may pass theair samples through a desiccant material that reduces the moisturecontent of the air samples. The sensor chamber 201 (as well as theintake tube 123 and exhaust tube 125) may be a low-VOC material (e.g.,Teflon) to minimize the amount of VOC outgassing, which could affect theoutput of the metal oxide sensors 220. In the embodiments describedabove where the sensor unit 120 includes two intake tubes 123 to collectair samples from two different heights, two intake pumps 223 are used,with air samples cycled alternately between intake tubes 123. In someembodiments, the sensor chamber 201 may also be configured to collectactual air samples for later analysis in a laboratory. For example, amicroprocessor command to a servo may open an air inlet on a sampleflask or bag (e.g., when the sensor unit 120 detects gas readings abovecertain levels). In those embodiments, for instance, the sensor unit 120may alert the data analysis and reporting system 180 that an air samplehad been collected and is available for pick-up and laboratory analysis.

As described above, the gas detection system 100 is able todifferentiate between natural gas leaks and volatile organic compoundsbecause, rather than relying on data from a single sensor, the sensorsuite 200 captures datapoints from an array of metal oxide sensors 220that each have a different responsiveness to methane and volatileorganic compounds. Meanwhile, the responsiveness of each metal oxidesensor 220 to each particular gas varies depending on the temperature ofthe heater element of that metal oxide sensor 220. In some embodiments,the sensor unit 120 takes advantage of the temperature-dependentresponsiveness of each metal oxide sensor 220 to collect even moredatapoints that can be used by the system 100 to identify, quantify, andlocate natural gas leaks. Specifically, in those embodiments, the sensorunit 120 heats and cools one or more of the metal oxide sensors 220 andcollects the sensor data output by each metal oxide sensor 220 atdifferent time periods when that metal oxide sensor 220 has been heatedor cooled to a different temperature. Because the responsiveness of themetal oxide sensor 220 to each gas is different at each of thosedifferent temperatures, the sensor data from that metal oxide sensor 220at those different time periods can effectively be treated as sensordata from separate sensors 220.

FIG. 2B is the basic measuring circuit of a metal oxide sensor 220. Asshown in FIG. 2B, a circuit voltage V_(C) (e.g., 5 volts DC) is appliedacross the sensor element, which has a resistance R_(s) between thesensor's two electrodes and the load resistor R_(L) connected in series.The sensor signal is measured indirectly as a change in voltage V_(RL)across the load resistor R_(L). A heater voltage V_(H) is applied to aheating plate (e.g., an RuO2 heater), which heats the sensing material.Variation in the heater voltage V_(H) changes the response of the sensor220 to various gases. Therefore, the heater voltage V_(H) is typicallykept constant (e.g., 5.0V±0.2V AC or DC).

FIG. 2C illustrates a sensor heater circuit 228 for a metal oxide sensor220 according to an exemplary embodiment. As shown in FIG. 2C, theheating plate of the metal oxide sensor 220 is controlled by the sensorheater circuit 228. The sensor heater circuit 228 may be, for example, aMOSFET switch.

FIG. 2D is a graph depicting an active temperature modulation processaccording to an exemplary embodiment. In the embodiment of FIG. 2D,sensor heater circuit 228 is closed (e.g., in response to a command froma microcontroller), causing the heating plate of the metal oxide sensor220 to heat. After a first predetermined time period, sensor data iscollected from the metal oxide sensor 220 (the “ramp up” measurement) asthe temperature of the metal oxide sensor 220 is increasing. After asecond predetermined time period, sensor data is collected from themetal oxide sensor 220 (the “stable temperature” measurement) as thetemperature of the metal oxide sensor 220 is stable. The sensor heatercircuit is then opened, allowing the sensor metal oxide sensor 220 tocool. After a third predetermined time period, sensor data is collectedfrom the metal oxide sensor 220 (the “ramp down” measurement) as thetemperature of the metal oxide sensor 220 is falling. The sequence maythen be repeated.

Switching the sensor heating plate on and off for predetermined timeperiods as described above yields three distinct time periods duringwhich the response of the metal oxide sensor 220 may vary: one duringthe temperature rise, one during a stable temperature, and one duringcooling. (In other embodiments, more complex thermal cycling may beemployed, for example that yield sine wave or saw-tooth patterns oftemperature variation.) For each of the metal oxide sensors 220 that isbeing cycled, the implementation described above would result in threeindividual resistances per time step, which can be viewed as equivalentto having three different sensors with slightly different sensitivities.In some embodiments, the number of distinct time periods may be limitedto three to limit the extra data being transmitted to the data analysisand reporting system 180. In other embodiments, however, additionalsensor data may be collected to better inform the automatedclassification methods described below.

FIG. 2E is an image of the sensor suite 200 according to an exemplaryembodiment. In the embodiment of FIG. 2E, the sensor suite includes thearray of metal oxide sensors 220 (including the methane-sensitive MOS240, the VOC-sensitive MOS 260, and the VOC-filtered MOS 280) as well asa temperature and relative humidity sensor 210.

FIG. 2F is an image of the sensor suite 200 inside the sealed sensorchamber 201 according to an exemplary embodiment. As shown in FIG. 2F,the intake pump 223 introduces an air sample into the sensor chamber 201via the intake tube 123, where the sensor suite 200 is exposed to theair sample for a predetermined sampling time period before the airsample is evacuated via the exhaust tube 125. As described above, thesealed sensor chamber 201 (and the heat generated by the metal oxidesensors 220) limit variations in the temperature and humidity of the airsamples, enabling the system 100 to more accurately differentiatebetween natural gas leaks and unrelated variations in the atmosphericconditions at the site 10.

Sensor Unit

FIG. 3A is a block diagram of the sensor unit 120 according to anexemplary embodiment. In the embodiment of FIG. 3A, the sensor unit 120includes a power subsystem 320, a processing subsystem 330, an airsampling subsystem 340, a communications subsystem 350, aposition/attitude subsystem 360, a sensor control subsystem 370, and anenvironmental sensing subsystem 380.

The processing subsystem 330 includes a controller 332 and local storage334. The local storage 334 may be any non-transitory computer readablestorage media (e.g., a microSD card). The controller 332 may include anyhardware processing unit capable of performing the functions describedherein. For example, the controller 332 may be a flash microcontroller(e.g., a Microchip Technology SAMD21). The controller 332 may alsoinclude additional hardware processing units (e.g., dedicated hardwareconfigured to perform one or more of the specific functions describedherein). To send and receive data from other subsystems, the controller332 may include a universal asynchronous receiver/transmitter (UART),analog-to digital converters (ADCs), digital-to-analog converters (e.g.,to output data to external devices), and digital and/or analog ports.The controller 332 may also communicate with other subsystems using theInter-Integrated Circuit (I2C) serial communication protocol, the SerialPeripheral Interface (SPI) communication protocol, etc.

As described below, the processing subsystem 330 receives sensor data300 from the environmental subsystem 380 (via the sensor controlsubsystem 370), packages that sensor data 300, logs the packaged sensordata 300 in the local storage 334, and outputs the packaged sensor data300 to the data analysis and reporting system 180 via the communicationssubsystem 350.

The power subsystem 320 receives power from the power source 128 (e.g.,solar panel or AC power source), stores that power in a battery 324, andprovides DC power to each of the other subsystems. To do so, the powersubsystem 320 includes a charging regulator 322 that regulates the powerreceived from the power source 128, and voltage regulators 328 thatprovide DC power at the voltage level required by each component. Thestorage capacity of the battery 324 and the power source 128 (e.g., thesize of the solar panel and resulting current output) may be selected asappropriate for the site 10.

In embodiments where the power source 128 is a solar panel, the chargingregulator 322 may include a high definition voltage divider that allowsthe controller 332 to track the voltage output by the solar panel,including spikes caused by great amounts of sunlight. The powersubsystem 320 also includes a battery temperature monitor 325 thatdetects overheating of the battery and outputs an alert to thecontroller 332.

The voltage regulators 328 may include a power smoothing system thatreduces noise in the power signal. In embodiments where the power source128 is provided at the site 10, most of the components of the powersubsystem 320 (with the exception of power regulation and conditioning)may be replaced by a voltage regulator (for example, to reduce thevoltage of a 12-volt DC power source 128 to 3.3 volts) or an AC-to-DCconverter (for example, where the power source 128 is an AC powersource).

The sensor unit 120 also includes a backup battery 326 (e.g., a 800 mAhlipo battery), which is continuously charged by the main battery 324,that supplements the battery 324 during processes that require higheramounts of power (e.g., cellular transmission). The backup battery 326also provides power to a system monitor 327, a microcontroller (e.g., aMicrochip Technology ATtiny) that protects the sensor unit 120 insituations in which the main battery 324 does not provide sufficientpower to the sensor unit 120. In the embodiment of FIG. 3A, forinstance, the controller 322 regularly outputs a power status update tothe system monitor 327 indicating that the sensor unit 120 hassufficient power. If the system monitor 327 does not receive a powerstatus update from the controller 322 for a predetermined time period(e.g., 2 minutes), the system monitor 327 is configured to reset eachsubsystem of the sensor unit 120, including the controller 332 and thecommunications subsystem 350. If the battery 324 cannot supplysufficient power to the sensor unit 120, the system monitor 327 outputscontrol signals to the other subsystems to enter a low power mode(“sleep mode”) until the battery 324 is adequately charged by the powersource 128. Powered by the backup battery 326, the system monitor 327 isconfigured to cause the other subsystems to resume normal operation oncesufficient power is restored. Accordingly, the sensor unit 120 isself-powered and is able to adapt and automatically recover frominsufficient power.

The air sampling subsystem 340 includes a pump cycling circuit 342 thatcontrols the intake pump 223 to draw outside air into the sensor chamber201 in response to pump cycling control signals output by the controller332. The controller 332 initiates air sampling cycles at a preprogrammedsampling rate. However, in the event that the battery 324 is low onpower, the controller 332 is configured to reduce that samplingfrequency to conserve power until the battery 324 is adequately chargedby the power source 128. Additionally, in cases of extreme environmentalconditions such as excessive cold or heat, the controller 332 can pausethe air intake cycle to minimize stress on pumps and other components.

The air sampling subsystem 340 also includes a pump vibration monitor348 (e.g., a MEMS microphone) that outputs information indicative of thevibration of the intake pump 223, enabling the controller 332 to monitorthe status of the intake pump 223.

The position/attitude subsystem 360 outputs information indicative ofthe location and orientation of the sensor unit 120 to the controller332. The position/attitude subsystem 360 includes a global positioningsystem (GPS) receiver 364 and a 3-axis accelerometer 368. The GPSreceiver 364, which may be incorporated in the cellular transceiver 353or may be a stand-alone GPS receiver 364, outputs information indicativeof the location of the sensor unit 120. The cellular transceiver 353 orGPS receiver 364 also outputs a clock signal used by the controller 332.The accelerometer 368 outputs information indicative of the orientationof the sensor unit 120, enabling the controller 332 to determine whetherthe mounting structure of the sensor unit 120 has shifted position orangle, which may occur under extreme winds.

The communications subsystem 350 outputs the packaged sensor data 300received from the controller 332 to the data analysis and reportingsystem 180 and reports the transmission status to the controller 332. Inthe embodiment of FIG. 3A, the communications subsystem 350 includes ashort-range wireless transceiver 351 for wirelessly communicating withother sensor units 120 (e.g., forming the mesh network 51 shown in FIG.1), a cellular transceiver 353 for communicating via cellular networks53, a WiFi transceiver 355 for communicating via WiFi networks 55, and asatellite transceiver 357 for communicating via satellite communicationsnetworks 57. Each of the transceivers includes an antenna 359. Theshort-range wireless transceiver 351 may be a XBEE3 Radio, whichcommunicates using the IEEE 802.15.4 wireless protocol. The cellulartransceiver 353 may use an Internet of Things (IoT) SIM card thatspecializes in small and remote data packets. The WiFi transceiver 355may be a single-board computer (e.g., a Raspberry Pi) that uses the sameantenna structure as the cellular transceiver 353.

As described above, in some embodiments, the sensor units 120 form aparent-child relationship where a primary sensor unit 120 sendscall-and-response attempts via the short-range wireless transceiver 351to trigger and receive the packaged sensor data 300 from secondarysensor units 120. Each short-range wireless transceiver 351 may utilizea high-powered antenna 359 with a 12 foot antenna height. The sensorunits 120 employ a rapid-fire call-and-response system developed byEarthview to help maintain wireless communication on sites 10 with a lotof vehicle traffic that can otherwise disrupt wireless signals. When thesensor units 120 form a parent-child relationship, the primary sensorunit 120 forwards the packaged sensor data 300 collected by both theprimary sensor unit 120 and secondary sensor units 120 to the dataanalysis and reporting system 180. In other embodiments, each sensorunit 120 outputs the packaged sensor data 300 to the data analysis andreporting system 180 directly.

The sensor unit 120 outputs the packaged sensor data 300 to the dataanalysis and reporting system 180 using calls to an applicationprogramming interface (API). In most instances, the cellular transceiver353 outputs the packaged sensor data 300 by making the call to the API.If a WiFi network 55 is available at the site 10, the WiFi transceiver357 (e.g., a Raspberry Pi) makes automated API calls (e.g., with apython script). When communicating via a WiFi network 55, the sensorunit 120 and the data analysis and reporting system 180 use acall-and-response system, enabling the sensor unit 120 to detect afailure to communicate via the WiFi network 55. If the WiFi transceiver357 is unable to successfully transmit the packaged sensor data 300 tothe data analysis and reporting system 180 via the WiFi network 55 for apredetermined time period (e.g., 2 seconds), the sensor unit 120 outputsthe packaged sensor data 300 via the cellular transceiver 353. Thecellular transceiver 353 also outputs custom responses to update thedata analysis and reporting system 180 on conditions such as WiFinetwork 55 outages, lack of power to the WiFi transceiver 357, andprogram failures. The sensor unit 120 can also operate in extremelyremote settings because the satellite transceiver 357 communicates via asatellite communications network 57 (e.g., an Iridium Satcom system)that is available anywhere on Earth with a view to the sky. Finally,even if the sensor unit 120 is in a location with no communicationsnetwork, the packaged sensor data 300 is logged in the local storage334, enabling the packaged sensor data 300 to be collected for analysis.

The controller 332 also outputs the status of the sensor unit 120 to thedata analysis and reporting system 180 via the communications subsystem350, enabling the status sensor unit 120 to be remotely monitored. Thestatus of the sensor unit 120 may include, for example, the voltageoutput by the solar panel (determined using the charging regulator 322),the temperature of the battery 324 (determined by the batterytemperature monitor 325), the status of the intake pump 223 (determinedusing the pump vibration monitor 348), and the orientation of the sensorunit 120 (determined by the accelerometer 368). Based on the orientationof the sensor unit 120, the data analysis and reporting system 180 mayoutput an alert (e.g., by email) if a sensor unit 120 has been knockedon its side by strong winds.

The environmental sensing subsystem 380 includes the temperature andrelative humidity sensor(s) 210 and the metal oxide sensors 220described above with reference to FIG. 2A and the anemometer 130described above with reference to FIG. 1. (Because only one anemometer130 is required at or near each site 10, some sensor units 120 may notinclude an anemometer 130.) Additionally, in the embodiment of FIG. 3A,the sensor unit 120 also includes an outdoor air quality sensor 382(e.g., a Renesas ZMOD4510 gas sensor platform). In some embodiments, thesensor unit 120 may include (e.g., outside the sensor chamber 201) oneor more particulate counters 384 (e.g., Plantower PM2.5 and PM10particulate matter sensors) and/or an atmospheric pressure sensor 386.

As described below with reference to FIG. 4, one benefit of the machinelearning-enabled gas leak detection process 400 is that additional datacan easily be incorporated to improve the accuracy of the gas leakdetection system 100. Meanwhile, the sensor suite 200 is expandable(e.g., a separate, detachable module to allow more installationflexibility) to accommodate any number of additional sensors (includingsensors developed in the future). Accordingly, in some embodiments, thesensor suite 200 may include (e.g., inside the sensor chamber 201) anadditional methane-sensitive MOSs 391 (e.g., a Renesas SGAS711solid-state chemiresistor sensor), a p-type metal oxide sensor 393(e.g., an Alphasense p-type metal oxide sensor), which are lesssensitive to variations in temperature and relative humidity than n-typemetal oxide sensors, and/or a carbon monoxide sensor 395 (e.g., a FigaroTGS5141-P00 carbon monoxide gas sensor).

Another benefit of the machine learning-enabled gas leak detectionprocess used by the gas leak detection system 100 is that it canincorporate sensor data 300 that would not be obviously associated withleak detection but that provides added machine-learning power forextracting leak-related signals. For example, in some embodiments, theenvironmental sensing subsystem 380 may include image sensors 397 and/ordirectional ultrasonic microphones 399. The directional ultrasonicmicrophones 399 may be used to detect sound emitted by leakingcomponents and determine the direction of the gas leak relative to thesensor unit 120. Finally, because the data analysis and reporting system180 can also incorporate data indicative of vibration or sound that iswithin human hearing range, the output of the pump vibration monitor 348may be output as part of the packaged sensor data 300 used to detect gasleaks.

The sensor control subsystem 370 supplies a voltage to the sensors ofthe environmental sensing subsystem 380, includes analog-to-digitalconverters (ADCs) 374 that convert analog voltages output by the sensorsof the environmental sensing subsystem 380 to digital sensor data 300,and outputs the digital sensor data 300 to the controller 332. (Toconvert the data output by the anemometer 130 to a signal that thecontroller 332 can interpret, the sensor control subsystem 370 may alsoinclude drop down resistors, filter capacitors, etc.) Because theresponsiveness of the metal oxide sensors 220 to specific gases isdependent on the temperature of those metal oxide sensors 220, thesensor control subsystem 370 also includes a sensor housing temperaturesensor 372 that monitors the external temperature of the housing of atleast one of the metal oxide sensors 220 (e.g., using a thermistor),which is included in the sensor data 300.

As described above with reference to FIGS. 2C-2D, in some embodimentsthe sensor unit 120 improves instrument sensitivity and gas selectivityby actively modulating the temperature of the heating plate of one ormore of the metal-oxide sensors 220. In those embodiments, for eachmetal oxide sensor 220 being heated, the sensor control subsystem 370includes a sensor heater circuit 228 that controls the sensor heatingplate in response to sensor control signals output by the controller332.

FIG. 3B is an exterior view of the sensor unit 120 according to anexemplary embodiment. FIG. 3C is an interior view of the sensor unit 120according to an exemplary embodiment.

Machine Learning-Enabled Gas Leak Detection Process

FIG. 4 is a flowchart illustrating a gas leak detection process 400according to an exemplary embodiment.

The gas leak detection system 100 generates a gas leak detection model430 indicative of the relationship between the sensor data 300 generatedby the sensor unit 120, the atmospheric conditions in the location ofthe sensor unit 120, and the concentrations of methane and other gases.In the embodiment of FIG. 4, the gas leak detection model 430 isgenerated by training a forward-learning neural network 420 usingtraining data 410 that includes sensor data 300 generated using thesensor unit 120 in locations with known concentrations 418 of methaneand other gases, and the measured temperature 412 and measured relativehumidity 414 in those locations. For instance, the training data 410 maybe collected in laboratory experiments using both the sensor unit 120and a reference-grade gas analyzer. Additionally or alternatively, themodel may be generated by installing a sensor unit 120 adjacent to airquality monitoring stations.

Among environmental conditions, the metal oxide sensors 220 are mostaffected by the actual amount of water in the air, which influences theresponsiveness of the metal-oxide sensor material to target gases.Therefore, the specific humidity 416 (i.e., the mass of water vapor perunit mass of air) is calculated to serve as an additional variable. Inaddition to affecting relative humidity 414, the air temperature 412also affects the temperature of the housings of the metal oxide sensor220, which in turn also affects the response of the metal oxide sensors220. Even though the relative humidity 414 is a function of airtemperature 412 and the specific humidity 416, it retains somepredictive power and is therefore included in the training data 410 usedto train the model 430.

The gas leak detection model 430 is then used to calculate measured gasconcentrations 490 at a site 10 that includes a sensor unit 120.

When sensor data 300 is observed by a sensor unit 120, the atmosphericconditions in the location of the sensor unit 120 are determined in step440, including the temperature 442 and relative humidity 444 observed bythe temperature and humidity sensors 210 and the specific humidity 446calculated using the observed temperature 442 and the observed relativehumidity 444. The gas leak detection model 430 is used in step 448 togenerate the predicted sensor data 450 that would be generated by thesensor unit 120 in those observed atmospheric conditions assuming thatno methane or gases are present in the air sample. In step 460, theobserved sensor data 300 is compared to the predicted sensor data 450 togenerate a sensor data comparison 470. In preferred embodiments, thesensor data comparison 470 is generated by dividing the observed sensordata 300 by the predicted sensor data 450. (In other embodiments, thesensor data comparison 470 may be generated by performing a differentcomparison, such as calculating the difference between the observedsensor data 300 and the predicted sensor data 450.)

Because the gas leak detection model 430 is generated using trainingdata 410 that includes known gas concentrations 418, the gas leakdetection model 430 can be used to identify events in the sensor data300 that best match the sensor data 300 gathered in the presence ofnatural gas. Additionally, the gas leak detection model 430 provides theability to attribute sensor data 300 to the presence of other gases.Accordingly, in step 480, measured gas concentrations 490 are calculatedusing the sensor data comparison 470 and the gas leak detection model430.

In other embodiments, the gas leak detection model 430 may be generatedby extracting a time series from a moving time window of sensor data 300and using that time series to calculate a non-linear (second order)multiple regression fit between the sensor data 300 (the dependentvariable, or predictand) and the measured air temperature 412, themeasured relative humidity 414, and the calculated specific humidity 416(as the predictor variables). However, the forward-leaning neuralnetwork 420 described above has the advantage of computationalefficiency. Once trained, the neural network 420 can be applied to eachnew observation in near real-time. Another advantage of aforward-leaning neural network 420 is that it is easier to make use of alarger set of inputs, such as using multiple temperature and humiditysensors 210, collecting multiple data points from metal oxide sensors220 during the active temperature modulation process (described abovewith reference to FIGS. 2C-2D), and using sensor data 200 fromdirectional microphones 397, the pump vibration monitor 348, anadditional methane sensor 391, a p-type metal oxide sensor 393, etc.

As described above, while metal oxide sensors are sufficiently sensitiveto natural gases for emissions monitoring needs, that sensitivity isswamped by their sensitivity to volatile organic compounds that may bepresent in the atmosphere, variations in the atmospheric moisturecontent and temperature, and the temperature of the sensor housingitself. The gas leak detection system 100 overcomes that drawback bytraining the neural network 420 on sensor data 300 from an array ofmetal oxide sensors 220 with different responses to different gases, oneor multiple temperature and relative humidity sensors 210, and a housingtemperature sensor 372. Meanwhile, the sensor units 120 are exposed to avariety of known gas concentrations 418. As a result, the training data410 used to train the neural network 420 includes a set of multi-sensorsignatures that represent the effects of different gas concentrations418, which are captured in the gas leak detection model 430. Whenultrasonic microphones 397 or image sensors 399 are included in thesensor data, the sounds or images are treated as an additional sensorinput. Similarly, vibration sensed by the pump-vibration monitor 348 istreated as additional information. When active temperature modulation isused (as described above with reference to FIGS. 2C-2D), the resultingfeature space data are classified using multivariate analysis methods.

Complementing this signature-capturing feature is the ability to useother sensor data 300 to help categorize what might be going on at amonitoring site 10 that could also affect the sensor data. For example,the measured gas concentrations 490 generated using the gas leakdetection model 320 might indicate a best match with an indistincthydrocarbon and carbon monoxide signature pattern. By using artificialintelligence-type if-then comparisons, the gas leak detection system 100can check to see if there are concurrent elevated readings inparticulates (as determined, for example, by the particulate counter384) and vibration (as determined, for example, by the pump vibrationmonitor 348). If so, the gas leak detection system 100 can flag theevent as being potentially due to the nearby operation of vehicles orother heavy equipment rather than an emissions event.

Accordingly, the gas leak detection system 100 capitalizes on thebenefits of the types of sensors used in the sensor suite 200 whileaddressing their inherent limitations using sensor fusion and artificialintelligence steps. Meanwhile, by using a remote data analysis andreporting center 180, where sophisticated classification and featureextraction tools can be applied and revised over time, the gas leakdetection system 100 can provide additional data post processing forfeature extraction, leak detection, and gas concentration measurement.

As described above, another drawback of metal oxide sensors 220 is thatthey suffer from degradation and/or a change in sensor response overtime. A common solution for gas sensors that experience degradation orsensor drift is to frequently calibrate those sensors using test gasses,which requires field visits or returning the instrument to themanufacturer. Another benefit of the gas leak detection process 400 isthat addresses degradation and sensor drift without requiringcalibration using test gasses. First, because the sensor data comparison470 is normalized by ratioing the predicted sensor data 450 and theobserved sensor data 300, with the predicted sensor data 450 calculatedbased on the observed atmospheric conditions, drift in the metal oxidesensors 220 becomes a non-factor. Second, atmospheric water vapor can beessentially treated as a calibration gas to which the metal oxidesensors 220 are continuously exposed. As described above, the neuralnetwork 420 models the response of the metal oxide sensors 220 to watervapor (the specific humidity 414), the temperature and humidity sensors210 measure the specific humidity 414 in the air samples continuallythroughout the deployment of the sensor unit 120, and the expectedsensor response to the amount of water vapor present (the predictedsensor data 450) is routinely calculated. Therefore, by comparing thesensor response to water vapor over time to the response seen afterinitial deployment, sensor degradation and sensor drift is quantifiedand a determination is made as to when a sensor 220 has becomeinsufficiently responsive and requires replacing.

The training data 410 is stored by the data analysis and reportingsystem 180 (e.g., in the storage media 186) and the gas leak detectionmodel 430 is generated by the server 182. In some embodiments, thesensor unit 120 outputs the packaged sensor data 300 to the dataanalysis and reporting system 180 (as described above with reference toFIG. 3A), which generates the measured gas concentrations 490. In otherembodiments, the sensor unit 120 stores the gas leak detection model 430(e.g., in the local storage 334).

In those embodiments, the controller 332 may use the gas leak detectionmodel 430 to generate the predicted sensor response 450 for the observedatmospheric conditions, compare the observed sensor data 300 to thepredicted sensor data 450, and convert the sensor data comparison 470 tomeasured gas concentrations 490. In those embodiments, the gas leakdetection model 430 may be stored in the local storage 334 at the timeof manufacture and updated over time, for example by the server 182updating the firmware over a network 50.

In other embodiments, the local controller 332 may generate coarse gasconcentration measurements and the server 182 may generate finer, moreaccurate measurements. To reduce the amount of data transferred, forexample, the local controller 332 may generate coarse measurements at ahigher sampling rate than the server 182 and the server may generatemore accurate measurements at a lower sampling rate than the localcontroller 332. In another example, the local controller 332 maygenerate a number of measurements and periodically output the maximumand average measured gas concentrations 490 generated over apredetermined time interval.

Results

FIG. 5A depicts the gas leak detection system 100 deployed for methaneemissions testing and evaluation by the Methane Emissions TechnologyEvaluation Center (METEC) under a variety of weather conditions and gasrelease rates. Two gas leak detection systems 100 were deployed. Thefirst gas leak detection system 100 included a primary sensor unit 120P,and two secondary sensor units 120A and 120B. The second gas leakdetection system 100 included a primary sensor unit 121P, and twosecondary sensor units 121A and 110B. As shown in FIG. 5A, METECreleased methane from one source 501 at various times and at variousemission rates throughout the day and night. The times of gas emissionand the emission rates were never shared during the testing and wereonly obtained once measured gas concentrations 490 were submitted toMETEC at the conclusion of the test. The weather during the test variedwildly and included high winds, a light blizzard, low temperatures, anda sunny day, providing a robust test of emissions detection,quantification, and overall performance of the system 100.

FIG. 5B is a graph of the emission rates measured by all six sensorunits 120 when METEC did not release gas. FIG. 5C is a graph of theemission rates measured by all six sensor units 120 when METEC releasedgas. METEC released methane at rates varying between 0.2 kg/hour and 2.3kg/hour. FIGS. 5B and 5C show a clear difference in the measuredemission rates when METEC released methane relative to when METEC didnot release gas.

FIG. 5D is a graph of the methane concentration measured by the gas leakdetection system 100 when METEC did not release gas. FIG. 5E is a graphof the methane concentration measured by the gas leak detection system100 when METEC released gas.

FIG. 5F is a graph of the emission rate measured by the primary sensorunit 120P during a selected time period. FIG. 5G is a graph of theemission rate measured by the primary sensor unit 121P during theselected time period. During the selected time period, METEC releasedgas at a rate of 0.7 kg/hour, stopped releasing gas, then released gasat a rate of 2.1 kg/hour. Winds favored measurements from node 120P(Northwest) and 121P (East). Both the primary sensor unit 120P and theprimary sensor unit 121P were approximately 130 feet from the source501.

A “difference of means” analysis was run on the entire time series ofdata to determine whether the calculated emissions rates for METEC weredifferent for (1) METEC gas flow “gas off” versus “gas on” and (2) METECgas flow at low rate (˜0.6-1.2 kg/hour) vs. high rate (1.2-2.1 kg/hour).In both situations, the calculated emissions rates are significantlydifferent at a 99% confidence level. Therefore, we can conclude that thegas leak detection system 100 found significant differences between whenMETEC was releasing gas or not. In other words, the gas leak detectionsystem 100 detected the releases. And we can conclude that the gas leakdetection system 100 was able to discriminate between periods of low gasrelease versus periods of high release.

The gas leak detection system 100 demonstrated the ability to detectfine changes in methane concentrations under field conditions, includingthe ability to account for changes in background air conditions. Theresults of the testing and evaluation suggest that the gas leakdetection system 100 can detect a natural gas emission rate of at least1.0 kg/hour at distances ranging from 130 to 180 feet from the source501. In this test, the gas leak detection system 100 could detect amethane increase above background of at least 0.3 ppm. Finally, the fullresults of the testing and evaluation suggest that the gas leakdetection system 100 can quantify emission rates with a reasonableaccuracy in typical conditions. Given these results, the objectiveconclusion is that the gas leak detection system 100 excels as acontinuous emissions monitoring platform, far exceeding minimumdetections limits (MDL) set forth by the EPA (10 kg/hour MDL) and MiQCertification (25 kg/hour as MDL).

Each sensor unit 120 may operate as a stand-alone sensing system thatdirectly communicates with a user device (e.g., cell phone, desktopcomputer) via a custom application. However, the gas leak detectionsystem 100 provides additional advantages when multiple sensor units 120are used to model and determine emission rates across a site 10.

Gas Leak Emission and Dispersion Modeling

Measuring gas concentrations from a single location is insufficient toidentify the likely source of a gas leak because the gas concentrationat the location of a sensor also depends on the distance from theemission source to the sensor and the dispersion and mixing along thatdistance. Measuring gas concentration without also measuring influencingfactors like wind conditions (or extrapolating atmospheric conditionsfrom distant measurements or relatively low-resolution numericalforecast models) limits the ability to convert gas concentrationmeasurements to emission rates and total emissions. And even highlysophisticated instruments that measure gas concentrations at impressiveaccuracies—but that do so at only a single location and/or infrequenttime intervals—are only able to provide a crude range of possibleemission rates for locations on a site 10.

As described above, the low cost sensor units 120 enable gasconcentrations 590 to be measured at regular intervals from multiplelocations at or near a site 10. Meanwhile, the sensor units 120 alsomeasure atmospheric conditions (wind speed, wind direction, airtemperature) at the site 10. Accordingly, as described below, the gasleak detection system 100 is able to model gas dispersion (in two- orthree-dimensions) across the site 10 and identify the emissions rates ateach point in that two- or three-dimensional space that when dispersedaccording to the model, match the gas concentrations 590 measured by thesensor units 120 at or near the site 10. By visualizing the emissionsrates across the site 10, the gas leak detection system 100 helpspinpoint the likely source of the gas leak.

FIG. 6 is a flowchart illustrating a high-level overview of the gas leakemission and dispersion modeling process 600 according to an exemplaryembodiment. The gas leak emission and dispersion modeling process 600may be performed by the data analysis and reporting system 180, forexample by receiving the data described below from the sensor units 120(and, where applicable external sources 60), storing that data in thecomputer readable storage media 186, and performing the processing stepsdescribed below by the server 182.

At each site 10, a number of potential emission sources 740 (a well head12, a separator 14, a storage tank 16, a pipeline, etc.) are identifiedor the site 10 is segmented into a number of (two- or three-dimensional)grid cells 730.

The gas leak emission and dispersion modeling process 600 begins withtwo pre-processing steps: In the process 700 (described below withreference to FIGS. 7A and 7B), the distances and bearings are identifiedbetween each sensor unit 120 and each grid cell 740 or potentialemission source 750. Additionally, in the process 800 (described belowwith reference to FIGS. 8A and 8B), the wind directions are identifiedbetween each sensor unit 120 and each grid cell 740 or potentialemission source 750.

Then, for each time step, the background concentrations from off-sitewinds are calculated in the process 900 (described below with referenceto FIG. 9) and the measured gas concentration 490 received from thesensor unit 120 is adjusted to identify the background-adjusted gasconcentration 1020 in the process 1000 (described below with referenceto FIG. 10), and a gas transport model 1400 (e.g., an inverse Gaussianplume dispersion model) is used to calculate the emission rate 1440, thetotal emissions 1460, and the wind-sensor unit intersections 1570, foreach pair of sensor unit 120 and grid cell 740 or potential emissionsource 750 in the process 1400 (as described below with reference toFIG. 14) based on the background-adjusted gas concentration 1020 and thewind direction 610 (determined, for example, by the anemometer 130 atthe site 10). To do so, the solar insolation is calculated using thetime 1102, the date 1106, and the location 1120 in the process 1100 (asdescribed below with reference to FIG. 11), sky cover is estimated inthe process 1200 (as described below with reference to FIG. 12), and thesurface layer air mixing conditions are estimated in the process 1300(as described below with reference to FIG. 13).

A leak location probability array 1590 identifying the most probableleak location is generated in the process 1500 (as described below withreference to FIGS. 15A-C). Total emissions for desired time periods arecalculated in the process 1600 (as described below with reference toFIG. 16). Emissions data and alerts are output in the process 1700 (asdescribed below with reference to FIG. 17), for example via thedashboard 1800 (as described below with reference to FIGS. 18A through18F).

FIG. 7A is a flowchart of an automated distance and bearingidentification process 700 a according to an exemplary embodiment. Inthe embodiment of FIG. 7A, bearings 720 and distances 730 between eachsensor unit 120 and each grid cell 740 are identified and stored in adistance-bearing map 760. A map 702 or aerial image 704 of the site 10is oriented to true north in step 706 and rasterized in step 708. Aselected grid resolution 710 is received and a grid is superimposed overthe map 702 or aerial image 704 to generate two- or three-dimensionalgrid cells 730 across the site 10 in in step 712. The grid celllocations of each sensor unit 120 are identified in step 714. Thebearing 720 from each sensor unit 120 to each grid cell 740 is measuredin step 722 and the distance 730 between each sensor unit 120 and eachgrid cell 740 is measured in step 723.

Additionally, a grid mask 780 is generated indicating the grid cells 740that include a potential emissions source 750 (e.g., a well head 12, aseparator 14, a storage tank 16, a pipeline, etc.) in step 755. The gridmask 780 may be manually generated. Alternatively, the grid mask 780 maybe generated by using an object detection algorithm to identifypotential emissions sources 750 in the aerial image 704 of the site 10.In embodiments where the grid cells 740 are three dimensional, potentialemissions sources 750 may be modeled in three-dimensions (e.g., with thepotential emissions source 750 being located at the top of a structurewhere a leak is most likely to occur).

FIG. 7B is a flowchart of a manual distance and bearing identificationprocess 700 b according to an exemplary embodiment. In some embodiments,a site 10 may include a limited number of sensor units 120 and a limitednumber of potential emissions sources 750. In those embodiments, thedistance-bearing map 760 can be manually generated by identifying thepotential emissions sources 750 in step 755, measuring the bearing 720from each sensor unit 120 to each potential emissions source 750 in step752, and measuring the distance 730 from each sensor unit 120 to eachpotential emissions source 750 in step 753.

FIG. 8A is a flowchart of an automated wind direction identificationprocess 800 a according to an exemplary embodiment. In the embodiment ofFIG. 8A, for each potential wind direction 610, a determination is madein step 820 as to whether the wind direction 610 intersects with a gridcell 740 and a sensor unit 120. If not (step 820: No), that cell isflagged in step 830 as being likely to be contributing to the sensorreading of the sensor unit 120. If so (step 820: Yes), the sensor unit120 and grid cell 740 pair is flagged as being a target pair foremission calculation in step 840. Those flags are stored in a sensorunit wind angle look-up table 850.

FIG. 8B is a diagram of a manual wind direction identification process800 b according to an exemplary embodiment. In the embodiments describedabove with a limited number of sensor units 120 and a limited number ofpotential emissions sources 750, the wind directions 0 intersecting eachpotential emissions source 750 and each sensor units 120 can be manuallyidentified and stored in the wind angle look-up table 850.

FIG. 9 is a flowchart illustrating a background concentrationcalculation process 900 according to an exemplary embodiment. In theembodiment of FIG. 9, for the current wind direction 610, adetermination is made in step 910 (using the sensor unit wind anglelook-up table 850) as to whether the wind crossed a grid cell 740 orpotential emissions source 750 and the sensor unit 120. If so (Step 910:Yes), the event is classified as a sensor unit-wind intersection in step920. If not (Step 910: No), the measured concentration 530 is recordedas an off-site value in step 930 and the average of all off-siteconcentrations are recorded in step 940. If, in step 950, adetermination is made that the wind did not cross any grid cell 740 orpotential emissions source 750 for any sensor unit 120, the event isclassified as an off-site wind case in step 960. The average backgroundconcentration 780 is stored in step 970.

FIG. 10 is a flowchart illustrating a background concentrationadjustment process 1000 according to an exemplary embodiment. In theembodiment of FIG. 10, the background concentration 780 for each timestep is subtracted from the measured concentration 490 for that timestep to form the background-adjusted concentration 1020 in step 1030.

FIG. 11 is a flowchart illustrating an example solar irradiancecalculation process 1100 for calculating solar irradiance 1120. As shownin FIG. 11, the time 1102 is converted to UTC in step 1104, the date1106 is converted to the Julian date in step 1108, and (using thelocation 1110) the relative sun position is calculated in step 1112, thesolar declination is calculated in step 1114, the solar constant iscalculated in step 1116, and the direct and diffuse solar irradiance1120 is calculated in step 1118.

FIG. 12 is a flowchart illustrating a sky cover estimation process 1200according to an exemplary embodiment. In the embodiment of FIG. 12, ifsky cover data is available from the instrumentation at the site 10(step 1210: Yes), that sensor data is converted to a sky cover estimate1280 in step 1220. If sky cover data is not available from theinstrumentation at the site 10 (step 1210: No), a determination is madein step 1230 as to whether sky cover data is available from an externalsource 60 (e.g., the National Environmental Satellite Data andInformation Service, Solcast, the nearest National Weather ServiceAutomated Surface Observation System, etc.). If so (step 1230: Yes), theexternal source 60 is queried in step 1240 and reformatted in step 1250to form the sky cover estimate 1280. If sky cover data is not availablefrom the instrumentation at the site 10 (step 1210: No) or an externalsource 60 (step 1230: No), a default sky cover estimate is identified instep 1260 and used as the sky cover estimate 1280.

FIG. 13 is a flowchart illustrating a surface layer air mixingconditions estimation process 1300 according to an exemplary embodiment.In the embodiment of FIG. 13, the Pasqill stability class 1310 isdetermined in step 1320 using a Pasquill stability class look-up table1330 adapted from published literature and the solar irradiance 1120(determined as described above with reference to FIG. 11), the sky coverestimate 1280 (determined as described above with reference to FIG. 12),the wind speed 1302 (determined, for example, by the anemometer 130 atthe site 10), the wind variability 1304 (determined based on variance inthe wind speed 1302 and wind direction 610 measurements over time, anexternal weather data source, etc.), the time 1102 of day, and thedistance 730 from the sensor unit 120 to the grid cell 740 or thepotential emissions source 750. External stability data 1340 fromexternal sources 60, such as the U.S. Environmental Protection Agency(EPA) rawindsonde observations from online sources (e.g., University ofWyoming soundings database for North America), or estimations fromforecast model output may also be used to identify the Pasquil stabilityclass 1310. Lateral and vertical dispersion coefficients 1360 arecalculated using the Pasquill stability class 1310 and dispersioncoefficient equations 1370 generated by calculating regression fits fordata in tables of lateral and vertical dispersion parameters publishedby EPA in step 1380. In situations of very light winds (under 1.1 m/s)and/or stable surface layer conditions, the dispersion coefficients 1360may be modified to account for added lateral mixing due to meandering.

FIG. 14 is a flowchart illustrating a gas transport model 1400 accordingto an exemplary embodiment. In the embodiment of FIG. 14, the gastransport model 1400 (e.g., an inverse Gaussian plume dispersion model)models the dispersion at the site 10 using the dispersion coefficients1360 and other data and identifies the emission rate 1440 at theselected grid cell 740 or potential emission source 750 that would bemost likely to cause the background-adjust concentration 1020 at theselected sensor unit 120. To model the dispersion at the site 10, thegas transport model 1400 may also be provided with the distance 730 fromthe sensor unit 120 to the grid cell 740 or the potential emissionssource 750, the wind speed 1302 (determined, for example, by theanemometer 130 at the site 10), the sensor unit height 1404 (e.g., theheight 1404 of the opening of the intake port 122 or intake tube 123),and the height 1406 of the potential emissions source 750 or object onthe grid cell 740. The system 100 also specifies an offset distance 1408from the sensor unit 120 to the plume centerline. For example, the plumecenterline may be assumed to align with the sensor unit 120 and the gridcell 740 (i.e., an offset distance 1408 of zero). However, thecalculations may be performed with different assumed offset distances1408, for to provide a range of potential emission rates 1440 perobservation or if wind direction 610 is known to a fine angleresolution. In some embodiments, additional data may be provided to andused by the gas transport model 1400. For example, in the embodimentsdescribed above where the sensor unit 120 includes two intake tubes 123to collect air samples from two different heights, the observedtemperature 442 at both heights may be provided to help the gastransport model 1400 define the vertical stability of the atmosphere (akey parameter defining the prescribed vertical and lateral mixing of aplume) and the shape of the concentration distribution in the verticaldimension (a key element of, for example, a Gaussian plume model).

The gas transport model 1400 may be a Gaussian plume model.Additionally, for calm, stable conditions, a lateral diffusion model maybe used in place of the wind-driven Gaussian plume dispersion model.

To calculate the total emissions 1460 for the selected time step, theemission rate 1440 is multiplied by the duration 1462 of the time stepin step 1466. The gas transport model 1400 may also identify aconfidence estimation 1450 for the emission rate 1440 identified and thegas transport model 1400 may flag any calculated emission rate 1400 ashaving a particularly high or low confidence estimation 1450 if thatconfidence estimation 1450 meets or exceeds predetermined confidencethreshold.

FIG. 15 illustrates a wind-cell intersection tracking array updateprocess 1500 according to an exemplary embodiment. In the embodiment ofFIG. 15, the gas leak detection system 100 stores a concentrationtracking array 1520 that includes the background-adjusted concentrations1020 measured by each sensor unit 120, an emission rate array 1540 thatincludes the emission rates 1440 calculated by the gas transport model1400 for each pair of sensor unit 120 and grid cell 740 (or potentialemissions source 750), an emissions total array 1560 that includes thetotal emissions 1460 for each pair of sensor unit 120 and grid cell 740(or potential emissions source 750), a wind-unit intersection array 1570that includes the number of intersections 1572 when the wind direction610 intersected each sensor unit 120 and grid cell 740 (or potentialemissions source 750), information indicative of the intersections eachsensor unit 120 and each grid cell 740 (or potential emissions source750) for each observations, the number of elapsed observations 1574since the wind direction 610 intersected each sensor unit 120 and eachgrid cell 740 (or potential emissions source 750). The aforementionedarrays hold a variety of information valuable for assessing how well thesensor units 120 are sampling the site (a function primarily ofplacement of the sensor units 120 relative to prevailing wind directions610), which sensor units 120 tend to observe the highest gasconcentrations 1020, and which combinations of sensor units 120 and gridcell 740 (or potential emissions source 750) are linked most often bywind patterns.

In the embodiment of FIG. 15, if the wind direction 610 intersects asensor unit 120 and grid cell (or potential emissions source 750) instep 1510, the concentration tracking array 1520 is updated in step1522, the emission rate array 1540 is updated in step 1542, theemissions totals array 1560 is updated in step 1562, and the sensorunit-wind intersection array 1572 is updated in step 1574.

If the background-adjusted concentration 1020 or emission rate 1440meets or exceeds a predetermined threshold in step 1576, a leakdetection tracking array 1580, which includes the number of leak events1584 in each grid cell 740 or at each potential emission source 750meeting or exceeding that predetermined threshold, is updated in step1582. Additionally, a leak location probability array 1594 identifyingthe most probable leak location is identified in step 1594.

FIGS. 15B and 15C illustrate the process 1594 for identifying the mostprobable leak location according to an exemplary embodiment. In theexample of FIG. 15B, the site 10 includes a number of sensor units 120and a number of potential emissions sources 750 in a number of gridcells 740. Over a first time period, the wind direction 610 generallyfaces the sensor unit 120B, leading the sensor unit 120B to measure ahigher concentrations 1020 than sensor units 120P and 120A. At each timestep, the gas transport model 1400 estimates the emissions rate 1440 ineach grid cell 740 that would result in the concentration 1020 measuredby the sensor unit 120B. (Using the grid mask 780 of potential emissionssources 750 described above, the system 100 may be configured to onlycalculate the emissions rates 1440 in grid cells 740 that include apotential emissions source.)

As shown in FIG. 15C, the wind direction 610 generally faces the sensorunit 120B, leading the sensor unit 120P to measure a higherconcentrations 1020 than sensor units 120A and 120B. Again, the gastransport model 1400 estimates the emissions rate 1440 in each grid cell740 that would result in the concentration 1020 measured by the sensorunit 120P. To identify the likely emitter source, the system 100 takesadvantage of the fact that the calculated emissions rate 1440 at a givengrid cell 740 should be similar when calculated using data fromdifferent sensor units 120. More specifically, the probability that eachgrid cell 740 includes the emitter source is inversely proportional tothe variance between the emissions rates 1440 for that grid cell 740calculated using the concentration 1020 measured by two different sensorunits 120. In the example of FIGS. 15B and 15C, for instance, the likelyemitter source is in grid cell 741, where the system 100 estimated thesame emissions rate 1441 (in this example, 1.6) using the concentration1020 measured by sensor units 120B and 120P.

Referring back to FIG. 15A, the leak location probability array 1590 isgenerated by calculating the variance between the emissions rates 1440for each grid cell 740 measured using sensor data 300 from two differentsensor units 120.

FIG. 16 is a flowchart illustrating a process 1600 for calculating thetotal emissions 1460 for a user-selected time period 1610 anduser-selected location (i.e., grid cells 740 or potential leak source750) according to an exemplary embodiment. As described above, theemissions totals array 1560 includes the total emissions 1460 for eachpair of sensor unit 120 and grid cell 740 (or potential emissions source750) for each time step 1602. Accordingly, the total emissions 1460 fora user-selected time period 1610 and a user-selected location can becalculated by summing the total emissions 1460 identified by each sensorunit 120 for each grid cell 740 (or potential emissions source 750)specified by the user and each time step 1602 in the time period 1610specified by the user. The grid mask 780 is applied to limit thecalculation of total emissions to only those grid cells 740 that includeprobable emissions sources 750.

FIG. 17 is a flowchart illustrating an emissions data output process1700 according to an exemplary embodiment.

As described above, the emission rate array 1540 includes the emissionrate 1440 for each pair of sensor unit 120 and grid cell 740 (orpotential emissions source 750) for each time step 1602. Accordingly,that data can be output to the user via a dashboard interface 1800(described below with reference to FIGS. 18A through 18F) that includestwo-dimensional and/or n-dimensional data visualization tools 1710.Additionally, in the embodiment of FIG. 17, the maximum or averageemission rate 1440 for the site 10 is plotted in step 1760. and, if theplotted emission rate 1440 meets or exceeds a predetermined alertthreshold in step 1780, an alert is output in step 1790 (e.g., viaemail, text message, and/or the dashboard interface 1800) to a partyresponsible for monitoring the site 10. As noted above, the grid mask780 is used to limit some calculations (such as emissions totals andaverage concentrations) to those cells known to be potential emissionsources. However, these masking steps may be done at the end of theprocessing and data presentation. It is therefore possible to search forpotential unknown locations of emissions, or to include those locationslater if new site information becomes available.

Dashboard and Alerts

FIG. 18A is a view 1800 a of the dashboard interface 1800 according toan exemplary embodiment. In the embodiment of FIG. 18A, the dashboardinterface 1800 includes a menu 1810 and a map view 1820 (with mapcontrols 1824) that enables users to view the locations of each site 10.

FIG. 18B is a view 1800 b of the dashboard interface 1800 according toan exemplary embodiment. In the embodiment of FIG. 18B, the dashboardinterface 1800 includes an alert 1790 regarding a site 10, including aconfidence rate 1450 that an emissions rate 1440 at the site 10 meets orexceeds the predetermined alert threshold. The dashboard interface 1800also provides functionality 1828 to view the emission rates 1440 foreach grid cell 740 (or potential emissions source 750) at the site 10using the two-dimensional (or n-dimensional) data visualization tools1710, for example as described below.

FIG. 18C is a view 1800 c of the dashboard interface 1800 according toan exemplary embodiment. In the embodiment of FIG. 18C, the map area1820 includes the map 702 or aerial image 704 of the site 10.

FIG. 18D is a view 1800 d of the dashboard interface 1800 according toan exemplary embodiment. In the embodiment of FIG. 18D, the view 1800 dincludes the alert 1790 and a map area 1820. The alert 1790 includes thetime 1891 of the potential leak event, the emissions rate 1440 duringthe leak event, the confidence rate 1450 identified by the gas transportmodel 1400 that an emissions rate 1440 meets or exceeds thepredetermined alert threshold, the duration 1892 of the leak event, andthe background-adjusted concentrations 1020 (e.g., the relativeconcentrations of methane and volatile organic compounds).

The map area 1820 includes grid cells 740 across the site 10 and thelocations of the sensor units 180 at the site 10. To visually indicatethe locations of likely emissions sources at the site 10, the grid cells740 that include a potential emissions source 750 (as determined by thegrid mask 780) are color coded (e.g., grid cell 1850 of FIG. 18D) basedon the emission rate 1440 or total missions 1460 at each grid cell 740.For example, grid cells 740 with increasing higher emission rates 1440or total emissions 1460 may be blue, yellow, and red. Additionally, thetransparency of the color may be proportional to the emission rates 1440or total emissions 1460. Accordingly, as shown in FIG. 18D, the datavisualization of the gas leak detection system 100 allows the user tovirtually walk through the emissions plumes at the site 10, providing apowerful tool for identifying the most likely leak sources on a site 10.

For each selected sample, the view 1800 d also includes thebackground-adjusted gas concentration 1020 (e.g., the measuredconcentration of methane and volatile organic compounds) as well as theenvironmental conditions 1830 (e.g., the air temperature 412, therelative humidity 414, the specific humidity 416, the atmosphericpressure, the wind speed 1302, the wind direction 610, etc.). The view1800 d also includes a graph area 1840 that displays the plottedbackground-adjusted gas concentrations 1020 for a user selected gas(e.g., methane, volatile organic compounds, etc.) or environmentalcondition 1830 (e.g., temperature (e.g., the air temperature 412, therelative humidity 414, the specific humidity 416, the atmosphericpressure, the wind speed 1302, the wind direction 610, etc.) selected,for example, using the graph control panel 1842.

FIG. 18E is a view 1800 e of the dashboard interface 1800 according toan exemplary embodiment. In the embodiment of FIG. 18E, the color-codedgrid cells 740 also include leak location probability array 1590,indicating to the user the most probable leak source at the site 10. Inthe absence of an alert 1790, the view 1800 e may include the totalemissions 1460 at the site 10 for default or a user-selected time period(e.g., one week, one month, the entire time period that the leakdetection system 100 has been monitoring the site 10, etc.).

FIG. 18G is a regional view 1800 g of the dashboard interface 1800according to an exemplary embodiment. In the embodiment of FIG. 18G, theview 1800 f includes the menu 1810 which provides functionality to viewadditional map layers, including imaging satellites (e.g., SententialSA-2 and LandSAT) for the geographic area of the site 10. The regionalview 1800 g allows the user or the gas leak detection system system 100to explore off-pad events and attempt to trace them back to the source.Potential regional sources (landfills, feedlots, other well pads) can beintegrated and displayed using the layers tab and, therefore, consideredwhen off pad emissions are detected. The regional view 1800 g alsoserves as a way to view imported emissions/concentration data such asaircraft, drone and some higher resolution satellite data, allowing theuser to build a complete picture of the current and historical emissionsprofile of the site 10.

As described above, the gas leak emission and dispersion modelingprocess 600 combines gas concentration observations 590, windobservations 610 and 1302, and the variability of winds over time(measured at an array of sensor units 120) with a sequence of mappingand mathematical modeling steps to convert the measured gasconcentrations 590 to emission rates 1440 assigned to grid cells 740spanning the site 10 of interest. By converting those point measurementsof gas concentrations 590 to spatial two- and three-dimensional maps1820 of gas emission rates 1440 and accumulating those calculations overtime and space, the gas leak detection system 100 both allows user tovisualize the gas plume and identifies the most likely location of a gasleak event.

While preferred embodiments of the gas leak detection system 100 havebeen described above, it is important to note that none of the featuresdescribed above are critical. While the sensor array 200 is describedabove as including metal oxide sensors 220 to measure the concentrationsof natural gas (specifically, methane) and volatile organic compounds,the machine learning-enabled gas leak detection process 400 can be usedto differentiate between—and measure the concentrations of—other gasesusing other sensors (even if, as described above, those sensors sufferfrom cross-sensitivities to those gases). Similarly, while the machinelearning-enabled gas leak detection process 400 is described above asovercoming the sensitivity of metal oxide sensors 220 to specifichumidity 446, temperature 442, relative humidity 444, and thetemperature of the sensor housing, the machine learning-enabled gas leakdetection process 400 can be used to compensate for sensorresponsiveness to any condition (environmental or otherwise). While thefeatures described above provide specific technical benefits when usedin combination, each of those features—including the sensor units 120,the data analysis and reporting 180, the sensor suite 200, the machinelearning-enabled gas leak detection process 400, the gas leak emissionand dispersion modeling process 600, and the dashboard interface 1800with two- and/or three-dimensional display visualization—may be usedseparately or with any combination of some or all of the aforementionedfeatures. Therefore, while preferred embodiments of the gas leakdetection system 100 have been described above, those skilled in the artwho have reviewed the present disclosure will readily appreciate thatother embodiments can be realized within the scope of the invention.Accordingly, the present invention should be construed as limited onlyby any appended claims.

What is claimed is:
 1. A gas leak detection system, comprising: a sensorsuite for sampling an air sample and outputting observed sensor data,the sensor comprising: one or more environmental condition sensors formeasuring the temperature and humidity of the air sample; and an arrayof metal oxide sensors, each of the metal oxide sensors having differentsensitivities to methane and different sensitivities to volatile organiccompounds; non-transitory computer readable storage media that stores agas leak detection model, generated by a machine learning algorithmtrained by exposing the sensor suite to known concentrations of methaneand volatile organic compounds having measured temperatures and measuredhumidity; and a processing unit that: identifies the temperature andhumidity of the air sample; uses the gas leak detection model togenerate predicted sensor data for a baseline air sample having nomethane or volatile organic compounds at the temperature and humidity ofthe air sample; compares the observed sensor data output by the sensorsuite to the predicted sensor data to generate a sensor data comparison;and uses the gas leak detection model to measure the methaneconcentration of the air sample based on the sensor data comparison. 2.The system of claim 1, further comprising: a sensor chamber enclosingthe sensor suite; an intake tube and an intake pump for introducing theair sample into the sensor chamber; and an exhaust tube for evacuatingthe chamber, wherein the metal oxide sensors heat the air sample insidethe sensor chamber.
 3. The system of claim 2, wherein: the metal oxidesensors each include a heating plate for heating the metal oxide sensor;the sensor suite includes a sensor housing temperature sensor thatmeasures an external temperature of at least one of the metal oxidesensors; the observed sensor data includes the external temperature ofthe at least one metal oxide sensor.
 4. The system of claim 3, furthercomprising: a heater circuit that provides a voltage to the heatingplate of the at least one metal oxide sensor; and a controller thatoutputs sensor control signals to the sensor heater circuit to heat andcool the at least one metal oxide sensor, wherein the observed sensordata includes data indicative of the output of the at least one metaloxide sensor at a plurality of external temperatures.
 5. The system ofclaim 1, wherein the one or more environmental condition sensorscomprise two or more temperature and humidity sensors, each of thetemperature and humidity sensors having different sensitivities anddifferent response times.
 6. The system of claim 1, wherein the array ofmetal oxide sensors comprises a methane-sensitive metal oxide sensor, avolatile organic compound-sensitive metal oxide sensor, and a volatileorganic compound-filtered metal oxide sensor.
 7. The system of claim 6,wherein the metal oxide sensors are n-type metal oxide sensors.
 8. Thesystem of claim 1, wherein the array of metal oxide sensors comprises ap-type metal oxide sensor.
 9. The system of claim 1, wherein the sensorsuite further comprises a particulate counter and/or a carbon monoxidesensor.
 10. The system of claim 1, comprising: a plurality of the sensorunits, each including the sensor suite, deployed at a site; ananemometer that senses wind speed and wind direction at the site; and aremote server that receives measured methane concentrations from theplurality of sensor units and uses a gas transport model to estimatemethane emissions rates at each of a plurality of location at the sitemost likely to cause the measured methane concentrations at each of thesensor units.
 11. A gas leak detection method, comprising: observingsensor data, by a sensor suite for sampling an air sample comprising oneor more environmental condition sensors for measuring the temperatureand humidity of the air sample and an array of metal oxide sensors, eachof the metal oxide sensors having different sensitivities to methane anddifferent sensitivities to volatile organic compounds; storing a gasleak detection model, generated by a machine learning algorithm trainedby exposing the sensor suite to known concentrations of methane andvolatile organic compounds having measured temperatures and measuredhumidity; identifying the temperature and humidity of the air sample;using the gas leak detection model to generate predicted sensor data fora baseline air sample having no methane or volatile organic compounds atthe temperature and humidity of the air sample; comparing the observedsensor data output by the sensor suite to the predicted sensor data togenerate a sensor data comparison; and using the gas leak detectionmodel to measure the methane concentration of the air sample based onthe sensor data comparison.
 12. The method of claim 11, wherein thesensor suite is enclosed in a sensor chamber and the metal oxide sensorsheat the air sample inside the sensor chamber.
 13. The method of claim12, wherein: the metal oxide sensors each include a heating plate forheating the metal oxide sensor; the sensor suite includes a sensorhousing temperature sensor that measures an external temperature of atleast one of the metal oxide sensors; the observed sensor data includesthe external temperature of the at least one metal oxide sensor.
 14. Themethod of claim 13, further comprising: providing, by a heater circuit,a voltage to the heating plate of the at least one metal oxide sensor;and outputting sensor control signals to the sensor heater circuit toheat and cool the at least one metal oxide sensor, wherein the observedsensor data includes data indicative of the output of the at least onemetal oxide sensor at a plurality of external temperatures.
 15. Themethod of claim 11, wherein the one or more environmental conditionsensors comprise two or more temperature and humidity sensors, each ofthe temperature and humidity sensors having different sensitivities anddifferent response times.
 16. The method of claim 11, wherein the arrayof metal oxide sensors comprises a methane-sensitive metal oxide sensor,a volatile organic compound-sensitive metal oxide sensor, and a volatileorganic compound-filtered metal oxide sensor.
 17. The method of claim16, wherein the metal oxide sensors are n-type metal oxide sensors. 18.The method of claim 11, wherein the array of metal oxide sensorscomprises a p-type metal oxide sensor.
 19. The method of claim 11,wherein the sensor suite further comprises a particulate counter and/ora carbon monoxide sensor.
 20. The method of claim 11, furthercomprising: observing methane concentrations by a plurality of thesensor units, each including the sensor suite, deployed at a site;sensing, by an anemometer, wind speed and wind direction at the site;and using a gas transport model to estimate methane emissions rates ateach of a plurality of location at the site most likely to cause themeasured methane concentrations at each of the sensor units.